Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/46289

TitleConvergence of asymptotic systems of non-autonomous neural network models with infinite distributed delays
Author(s)Oliveira, José J.
KeywordsNeural networks
Unbounded coefficients
Bounded coefficients
Infinite distributed delays
Boundedness
Global convergence
Asymptotic systems
Issue date25-Feb-2017
PublisherSpringer Verlag
JournalJournal of Nonlinear Science
Abstract(s)In this paper we investigate the global convergence of solutions of non-autonomous Hopfield neural network models with discrete time-varying delays, infinite distributed delays, and possible unbounded coefficient functions. Instead of using Lyapunov functionals, we explore intrinsic features between the non-autonomous systems and their asymptotic systems to ensure the boundedness and global convergence of the solutions of the studied models. Our results are new and complement known results in the literature. The theoretical analysis is illustrated with some examples and numerical simulations.
TypeArticle
URIhttp://hdl.handle.net/1822/46289
DOI10.1007/s00332-017-9371-8
ISSN0938-8974
e-ISSN1432-1467
Publisher versionhttps://link.springer.com/article/10.1007/s00332-017-9371-8
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

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